• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.712-722
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• 2021

The primary purpose of this study was to examine the mediating effect of cognitive emotion regulation strategies in the relationship between adult attachment and interpersonal relationship competence. To achieve the purpose of the study, college students from nationwide completed the questionnaire and analyzed 326 valid ones. Descriptive statistics, Pearson correlation analysis, and structural equation model were performed using SPSS 22.0 and AMOS 23.0. The results are as follows. First, adult attachment(attachment anxiety and attachment avoidance) were negatively correlated with interpersonal relationship competence. Also, attachment avoidance was significantly influenced on interpersonal relationship competence than attachment anxiety. Second, in the relationship between adult attachment and interpersonal relationship competence, attachment anxiety was fully mediated and attachment avoidance was partially mediated with cognitive emotion regulation strategies. Attachment anxiety and attachment avoidance mediated with adaptive cognitive emotion regulation strategies. Based on these results, we verified the importance and the possibility of changing the cognitive emotion regulation for increasing the level of interpersonal relationship competence. Finally, this study may provide an insight in terms of developing education and programs of adaptive cognitive emotion regulation strategies in counseling session.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1329-1341
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• 2022

Destroying the marine environment and marine ecosystem and causing marine accidents, marine debris is generated every year, and among them, submerged marine debris is difficult to identify and collect because it is on the seabed. Therefore, deep-learning-based semantic segmentation was experimented on waste fish nets and waste ropes using underwater images to identify efficient collection and distribution. For segmentation, a high-resolution network (HRNet), a state-of-the-art deep learning technique, was used, and the performance of each optimizer was compared. In the segmentation result fish net, F1 score=(86.46%, 86.20%, 85.29%), IoU=(76.15%, 75.74%, 74.36%), For the rope F1 score=(80.49%, 80.48%, 77.86%), IoU=(67.35%, 67.33%, 63.75%) in the order of adaptive moment estimation (Adam), Momentum, and stochastic gradient descent (SGD). Adam's results were the highest in both fish net and rope. Through the research results, the evaluation of segmentation performance for each optimizer and the possibility of segmentation of marine debris in the latest deep learning technique were confirmed. Accordingly, it is judged that by applying the latest deep learning technique to the identification of submerged marine debris through underwater images, it will be helpful in estimating the distribution of marine sedimentation debris through more accurate and efficient identification than identification through the naked eye.

• Korean Journal of Culture and Social Issue
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• v.28 no.3
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• pp.483-512
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• 2022

This study examined the patterns of coping strategies among Koreans during the early stage of the COVID-19 pandemic, explored the influence of demographic information (gender, age, economic level, household type), along with the unusual experiences due to COVID-19 (fear, stress of COVID, constraints of routine, income risk) on the classification of subclasses, and analyzed the latent profile differences in psychological wellbeing (life satisfaction, depression, and anxiety). An online survey was conducted among Korean Adults(n=600) between April 13, 2020 and 21, when WHO declared COVID-19 a global pandemic and Daegu as well as Gyeongsangbuk-do was nominated as a special disaster zone. First, Latent Profile Analysis (LPA) was used to identify subclasses of coping strategies and results suggested that the 4-class model had the best fit. Second, Class memberships were predicted by gender, age, economic level, as well as fear, stress, constraints of routine, and income risk, among the unusual experiences due to COVID-19. Finally, there are differences in psychological wellbeing among latent profiles. 'High level of adaptive coping group 3' showed the highest level of life satisfaction, 'Adaptive-maladaptive coping group 4' showed the highest level of depression, anxiety. Implications and suggestions are discussed based on the study results.

• The Journal of the Acoustical Society of Korea
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• v.27 no.3
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• pp.140-148
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• 2008

In this study, a numerical method for a time-domain acoustic wave backscattering analysis is established based on a physical optics and a Fourier transform. The frequency responses of underwater targets are calculated based on physical optics derived from the Kirchhoff-Helmholtz integral equation by applying Kirchhoff approximation and the time-domain signals are simulated taking inverse fast Fourier transform to the obtained frequency responses. Particularly, the adaptive triangular beam method is introduced to calculate the areas impinged directly by acoustic incident wave and the virtual surface concept is adopted to consider the multiple reflection effect. The numerical analysis result for an acoustic plane wave field incident normally upon a square flat plate is coincident with the result by the analytic time-domain physical optics derived theoretically from a conventional physical optics. The numerical simulation result for a hemi-spherical end-capped cylinder model is compared with the measurement result, so that it is recognized that the presented method is valid when the specular reflection effect is predominant, but, for small targets, gives errors due to higher order scattering components. The numerical analysis of an idealized submarine shows that the established method is effectively applicable to large and complex-shaped underwater targets.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.41-51
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• 2024

In this paper, we explore the enhancement of target detection accuracy in the guided weapon using deep learning object detection on infrared (IR) images. Due to the characteristics of IR images being influenced by factors such as time and temperature, it's crucial to ensure a consistent representation of object features in various environments when training the model. A simple way to address this is by emphasizing the features of target objects and reducing noise within the infrared images through appropriate pre-processing techniques. However, in previous studies, there has not been sufficient discussion on pre-processing methods in learning deep learning models based on infrared images. In this paper, we aim to investigate the impact of image pre-processing techniques on infrared image-based training for object detection. To achieve this, we analyze the pre-processing results on infrared images that utilized global or local information from the video and the image. In addition, in order to confirm the impact of images converted by each pre-processing technique on object detector training, we learn the YOLOX target detector for images processed by various pre-processing methods and analyze them. In particular, the results of the experiments using the CLAHE (Contrast Limited Adaptive Histogram Equalization) shows the highest detection accuracy with a mean average precision (mAP) of 81.9%.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.5
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• pp.335-347
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• 2013

In February 2008, high storm waves due to a developed atmospheric low pressure system propagating from the west off Hokkaido, Japan, to the south and southwest throughout the East Sea (ES) caused extensive damages along the central coast of Japan and along the east coast of Korea. This study consists of two parts. In the first part, we estimate extreme storm wave characteristics in the Toyama Bay where heavy coastal damages occurred, using a non-hydrostatic meteorological model and a spectral wave model by considering the extreme conditions for two factors for wind wave growth, such as wind intensity and duration. The estimated extreme significant wave height and corresponding wave period were 6.78 m and 18.28 sec, respectively, at the Fushiki Toyama. In the second part, we perform numerical experiments on wave-structure interaction in the Fushiki Port, Toyama Bay, where the long North-Breakwater was heavily damaged by the storm waves in February 2008. The experiments are conducted using a non-linear shallow-water equation model with adaptive mesh refinement (AMR) and wet-dry scheme. The estimated extreme storm waves of 6.78 m and 18.28 sec are used for incident wave profile. The results show that the Fushiki Port would be overtopped and flooded by extreme storm waves if the North-Breakwater does not function properly after being damaged. Also the storm waves would overtop seawalls and sidewalls of the Manyou Pier behind the North-Breakwater. The results also depict that refined meshes by AMR method with wet-dry scheme applied capture the coastline and coastal structure well while keeping the computational load efficiently.

• The Journal of Korean Academy of Sensory Integration
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• v.6 no.1
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• pp.47-62
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• 2008

Introduction : Sensory Integration(SI) theory, science it is developed by an occupational therapist A. Jean Ayres, is the one of the most popular frames of reference used in occupational therapy(OT) intervention. It has been proved as a scientific theory especially in neuroscience discipline through abundant research and practice. Occupational therapists apply the SI therapy with strong clinical reasoning to improve adaptive behaviors of their clients and try to link the adaptive behaviors with occupational performance in the clients' everyday life. One of the manners regarding clinical reasoning is Top-down approach. In occupational therapy discipline, Top-down approach is well-reflected within two evaluation tools; Canadian Occupational Performance Measure(COPM) and Assessment of Motor and Process Skills(AMPS) and two models of practice; Canadian Model of Occupational Performance(CMOP) and Occupational therapy intervention process model(OTIPM). Objective : The purpose of this paper demonstrates how SI therapy can be employed within OTIPM and how the OT process (evaluation-intervention-outcome) can be structuralized based on the Top-down approach. This single-case study recognizes the impact of a SI therapy for a male adolescent on his occupational performance. Intervention Examined : "P" was 16 years old male adolescent with no diagnosis and junior of the high school when he was referred. P was always with mouth opened, showed difficulties in gathering things need to be prepared and managing and paying money for shopping, and his colleges dislike getting close to him because he can't was his body well. AMPS was administrated in initial evaluation and reevaluation of P's occupation performance, Bruininks-Oserestky Test of Motor Proficiency-2(BOT-2) was carried out to assess motor functions and perception skills related in sensory integration, and occupational therapist performed clinical observation in order to complement the evaluation quantitatively and quantitatively. Based on the evaluation, it is concluded that the SI therapy is primary means to improve P's occupational performance, and three therapeutic approaches were constructed; restorative, acquired and compensatory approach. P showed improved motor and process skills in occupational performance after undergone the occupational therapy. Conclusions : The sensory integration therapy was practical enough to build the bridge between the occupational performance(Top) and the underlying component problems (Bottom). The OTIPM was helpful to identify meaningful occupation for P and P's family within P's contexts, and the AMPS was valuable to analyze and clarify the cause of difficulties in the chosen occupational performance.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.10
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• pp.529-538
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• 2007

Fair Queueing algorithms based on Generalized Processor Sharing (GPS) not only guarantee sessions with service rate and delay, but also provide sessions with instantaneous fair sharing. This fair sharing distributes server capacity to currently backlogged sessions in proportion to their weights without regard to the amount of service that the sessions received in the past. From a long-term perspective, the instantaneous fair sharing leads to a different quality of service in terms of delay and bandwidth to sessions with the same weight depending on their traffic pattern. To minimize such long-term unfairness, we propose a delay-bandwidth normalization model that defines the concept of value of service (VoS) from the aspect of both delay and bandwidth. A model and a packet-by-packet scheduling algorithm are proposed to realize the VoS concept. Performance comparisons between the proposed algorithm and algorithms based on fair queueing and service curve show that the proposed algorithm provides better long-term fairness among sessions and that is more adaptive to dynamic traffic characteristics without compromising its service rate and delay guarantees.

• Journal of KIISE:Software and Applications
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• v.37 no.2
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• pp.110-119
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• 2010

In this paper, we propose an automatic segmentation of the prostate using intensity distribution and gradient information in MR images. First, active shape model using adaptive intensity profile and multi-resolution technique is used to extract the prostate surface. Second, hole elimination using geometric information is performed to prevent the hole from occurring by converging the surface shape to the local optima. Third, the surface shape with large anatomical variation is corrected by using 2D gradient information. In this case, the corrected surface shape is often represented as rugged shape which is generated by the limited number of vertices. Thus, it is reconstructed by using surface modelling and smoothing. To evaluate our method, we performed the visual inspection, accuracy measures and processing time. For accuracy evaluation, the average distance difference and the overlapping volume ratio between automatic segmentation and manual segmentation by two radiologists are calculated. Experimental results show that the average distance difference was 0.3${\pm}$0.21mm and the overlapping volume ratio was 96.31${\pm}$2.71%. The total processing time of twenty patient data was 16 seconds on average.

• Structural Monitoring and Maintenance
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• v.2 no.1
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• pp.77-93
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• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.